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Record W2789412420 · doi:10.1002/jclp.22592

Effect of prenatal mindfulness training on depressive symptom severity through 18‐months postpartum: A latent profile analysis

2018· article· en· W2789412420 on OpenAlexfundno aff
Jennifer N. Felder, Danielle S. Roubinov, Nicole R. Bush, Kimberly Coleman‐Phox, Cassandra Vieten, Barbara Laraia, Nancy E. Adler, Elissa S. Epel

Bibliographic record

VenueJournal of Clinical Psychology · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteCanadian Institute for Advanced Research
KeywordsMindfulnessDepressive symptomsPsychologyClinical psychologyIntervention (counseling)Logistic regressionMultinomial logistic regressionDepression (economics)PsychiatryMedicineInternal medicineAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined whether prenatal mindfulness training was associated with lower depressive symptoms through 18-months postpartum compared to treatment as usual (TAU). METHOD: A controlled, quasi-experimental trial compared prenatal mindfulness training (MMT) to TAU. We collected depressive symptom data at post-intervention, 6-, and 18-months postpartum. Latent profile analysis identified depressive symptom profiles, and multinomial logistic regression examined whether treatment condition predicted profile. RESULTS: Three depressive symptom severity profiles emerged: none/minimal, mild, and moderate. Adjusting for relevant covariates, MMT participants were less likely than TAU participants to be in the moderate profile than the none/minimal profile (OR = 0.13, 95% CI = 0.03-0.54, p = .005). CONCLUSIONS: Prenatal mindfulness training may have benefits for depressive symptoms during the transition to parenthood.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.101
GPT teacher head0.483
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2018
Admission routes1
Has abstractyes

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